class_docstr: First version of Epistemic computation used as active learning strategy
class_docstr_tok: ["First","version","of","Epistemic","computation","used","as","active","learning","strategy"]
code: def _load_model(self, path, network):
      model_file = EpistemicScoring._get_model_path(path)
      if not model_file and not network:
          logger.warning(
              f"Skip Epistemic Scoring:: Model(s) {path} not available yet"
          )
          return None, None
  
      logger.info(f"Using {model_file} for running Epistemic")
      model_ts = (
          int(os.stat(model_file).st_mtime)
          if model_file and os.path.exists(model_file)
          else 1
      )
      if network:
          model = copy.deepcopy(network)
          if model_file:
              if torch.cuda.is_available():
                  checkpoint = torch.load(model_file)
              else:
                  checkpoint = torch.load(
                      model_file, map_location=torch.device("cpu")
                  )
              model_state_dict = checkpoint.get("model", checkpoint)
              model.load_state_dict(
                  model_state_dict, strict=self.load_strict
              )
      else:
          if torch.cuda.is_available():
              model = torch.jit.load(model_file)
          else:
              model = torch.jit.load(
                  model_file, map_location=torch.device("cpu")
              )
      return model, model_ts
  
file_path: MONAILabel/monailabel/tasks/scoring/epistemic.py
imports: import copy
  import logging
  import os
  import time
  import numpy
  import torch
  from monai.inferers import sliding_window_inference
  from monai.transforms import Compose
  from monai.utils import deprecated
  from monailabel.interfaces.datastore import Datastore
  from monailabel.interfaces.tasks.scoring import ScoringMethod
label_desc: Use image node_id 5 for calling the EpistemicScoring obj's underlying member method code with example usage: obj._load_model(path, network) and returns: model,  model_ts, None,  None